if (!require('knitr')) install.packages('knitr'); library('knitr')
knitr::opts_chunk$set(warning=FALSE, message=FALSE, fig.align='center')
###############
# Carbon and Nitrogen Isotopic Analysis of Individual Amino Acids in Montipora capitata
# Author: C. Wall
# Collaborators: Brian Popp, Ruth Gates
# Institution: University of Hawai'i at Mānoa
###############
# Load in packages
if (!require("pacman")) install.packages("pacman"); library(pacman) # for rapid install if not in library
pacman::p_load(devtools, ellipse, ggbiplot, vqv, patchwork, graphics, plyr, effects, MASS, tidyverse, dplyr, plotrix, vegan, cowplot, caret, reshape)
Techniques for Compound Specific Isotope Analysis (CSIA) of individual amino acids (AA) have been developed to better understand ecosystem food webs, trophic positions, and sources of nutrition in biological samples ranging from bacteria to cetaceans. Bulk tissue isotope analysis requires separate accounting for isotopic signatures at the base of the food web, which vary in across locations and time periods. However, CSIA can account for both source and trophic isotope effects in a single sample of a consumer’s tissue.
Source amino acids are a group of AA that exhibit little change in isotopic composition with increasing trophic levels and reflect the isotopic composition of the ‘source material’ at the base of the food web from which they originated.
Trophic amino acids on the other hand are a group of AA that show significant 15N enrichment compared to source-AA, which correspond to trophic steps.This enrichment is quite large and may exceed 8 ‰.
Carbon and nitrogen isotope values in plankton, Symbiodiniaceae symbionts and coral host tissues. With linear models testing effect of treatment and fraction
bulk<-read.csv("data/bulkCN.isotopes.csv")
bulk$Treat.Int<-factor(bulk$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
bulk$Fraction<-factor(bulk$Fraction, levels=c("host", "symb", "plank"))
bulk.HS<-bulk[!(bulk$Fraction=="plank"),] # plankton removed from fraction
bulk.d15N.means<-aggregate(d15N~Fraction+Treat.Int, data=bulk, mean, na.rm=TRUE); bulk.d15N.means
## Fraction Treat.Int d15N
## 1 host L-NF 5.35
## 2 symb L-NF 4.20
## 3 host L-F 5.35
## 4 symb L-F 4.05
## 5 host D-F 5.45
## 6 symb D-F 4.40
## 7 plank plank 7.10
bulk.d13C.means<-aggregate(d13C~Fraction+Treat.Int, data=bulk, mean, na.rm=TRUE); bulk.d13C.means
## Fraction Treat.Int d13C
## 1 host L-NF -16.60
## 2 symb L-NF -17.40
## 3 host L-F -16.55
## 4 symb L-F -17.60
## 5 host D-F -15.75
## 6 symb D-F -15.95
## 7 plank plank -21.80
bulk.CN.means<-aggregate(C.N~Fraction+Treat.Int, data=bulk, mean, na.rm=TRUE); bulk.CN.means
## Fraction Treat.Int C.N
## 1 host L-NF 6.201642
## 2 symb L-NF 6.711709
## 3 host L-F 6.259647
## 4 symb L-F 7.022590
## 5 host D-F 5.973318
## 6 symb D-F 5.328831
## 7 plank plank 4.418660
bulk.means<-cbind(bulk.d15N.means, bulk.d13C.means[3], bulk.CN.means[3])
### fig formatting
format.fig<-
theme(axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")),
axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(text = element_text(size=8)) +
theme(legend.text=element_text(size=10), legend.key = element_blank()) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black", size=0.5))
##### bulk figures
d13C.m<-aggregate(d13C~Treat.Int, data=bulk, mean, na.rm=TRUE)
d13C.n<-aggregate(d13C~Treat.Int, data=bulk, length)
# d13C
d13C.plot<-ggplot(bulk, aes(y=d13C, x=Treat.Int))+
geom_boxplot(aes(fill=Fraction)) +
geom_dotplot(aes(fill = Fraction, color = Fraction),
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{13}, C, " (\u2030, V-PDB)")))+
xlab("Treatment") + theme(legend.title = element_blank()) +
format.fig
anova(lm(d13C ~ Fraction+Treat.Int, data=bulk.HS))
## Analysis of Variance Table
##
## Response: d13C
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.4008 1.40083 1.5317 0.2510
## Treat.Int 2 3.7717 1.88583 2.0620 0.1896
## Residuals 8 7.3167 0.91458
# d15N
d15N.plot<-ggplot(bulk, aes(y=d15N, x=Treat.Int))+
geom_boxplot(aes(fill=Fraction)) +
geom_dotplot(aes(fill = Fraction, color = Fraction),
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{15}, N, " (\u2030, air)")))+
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(d15N ~ Fraction+Treat.Int, data=bulk.HS))
## Analysis of Variance Table
##
## Response: d15N
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 4.0833 4.0833 14.7702 0.004925 **
## Treat.Int 2 0.1050 0.0525 0.1899 0.830662
## Residuals 8 2.2117 0.2765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
####
# C:N
C.N.plot<-ggplot(bulk, aes(y=C.N, x=Treat.Int), na.rm=T)+
geom_boxplot(aes(fill=Fraction)) +
geom_dotplot(aes(fill = Fraction, color = Fraction),
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(C:N)))+
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(C.N ~ Fraction+Treat.Int, data=bulk.HS))
## Analysis of Variance Table
##
## Response: C.N
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.13168 0.13168 0.7228 0.41995
## Treat.Int 2 2.21760 1.10880 6.0861 0.02474 *
## Residuals 8 1.45749 0.18219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
####
# d13H-S
d13C.HS.plot<-ggplot(bulk.HS, aes(y=d13C.H.S, x=Treat.Int), na.rm=T)+
geom_boxplot() +
geom_dotplot(
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{13}, C[H-S], " (\u2030)"))) +
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(d13C.H.S ~ Treat.Int, data=bulk.HS))
## Analysis of Variance Table
##
## Response: d13C.H.S
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.76333 0.38167 7.8966 0.06378 .
## Residuals 3 0.14500 0.04833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# d15NH-S
d15N.HS.plot<-ggplot(bulk.HS, aes(y=d15N.H.S, x=Treat.Int), na.rm=T)+
geom_boxplot() +
geom_dotplot(
binaxis='y', stackdir='center', dotsize = 0.5, alpha=0.5,
position = position_dodge(0.75))+
ylab(expression(paste(delta^{15}, N[H-S], " (\u2030)")))+
xlab("Treatment") +
format.fig + theme(legend.position = "none")
anova(lm(d15N.H.S ~ Treat.Int, data=bulk.HS))
## Analysis of Variance Table
##
## Response: d15N.H.S
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.06333 0.031667 0.2879 0.7685
## Residuals 3 0.33000 0.110000
####
bulk.legend <- get_legend(
# create some space to the left of the legend
d13C.plot + theme(legend.box.margin = margin(0, 0, 0, 12)))
bulk.figures<-(d13C.plot+theme(legend.position = "none") | d15N.plot | C.N.plot | d13C.HS.plot | d15N.HS.plot |
bulk.legend)
print(bulk.figures)
Figure 1. Bulk tissue carbon and nitrogen isotope values for corals (host), their endosymbiont Symbiodiniaceae (symbiont), and a pooled plankton sample (plank) at three Light-by-Feeding nutrition treatments. L-NF (Light-Not Fed, autotrophic), L-F (Light-Fed, mixotrophic), Dark-Fed (heterotrophic). Boxplots are n=2, except for plankton (n=1).
ggsave("figures/Fig 1. bulk isotope.pdf", height=3.5, width=12, encod="MacRoman")
Carbon in amino acids of plankton, Symbiodiniaceae symbionts and coral host tissues.
######## ########
## Carbon
######## ########
rm(list=ls())
d13C.dat<-read.csv("data/d13C.CSIA.wide.csv") # wide form carbon data
colnames(d13C.dat)
d13C.dat$Fraction<-factor(d13C.dat$Fraction, levels=c("host", "symb", "plank"))
d13C.dat$Treat.Int<-factor(d13C.dat$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
d13C.dat<-d13C.dat[ , !(names(d13C.dat) %in% c("Norleucine", "Aminoadipic.Acid", "Methionine"))] #remove Norleucine, Methionine, Aminoadipic Acid
######## ########
## Nitrogen
######## ########
d15N.dat<-read.csv("data/d15N.CSIA.wide.csv") # wide form carbon data
d15N.dat<-d15N.dat[ , !(names(d15N.dat) %in% c("Norleucine", "Aminoadipic.Acid", "Methionine"))]
d15N.dat$Fraction<-factor(d15N.dat$Fraction, levels=c("host", "symb", "plank"))
## Permanova carbon
d13C.dat.perm<- d13C.dat[!(d13C.dat$Fraction=="plank"),]
df.manova.C<-d13C.dat.perm[, c(11:23)] # remove factor columns
df.manova.C.abs<-abs(df.manova.C) # change all to absolute values
set.seed(138)
perman.C<-adonis2(df.manova.C.abs~Fraction*Treat.Int, data=d13C.dat.perm, permutations=1000,
method="bray", sqrt.dist = TRUE)
perman.C
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## adonis2(formula = df.manova.C.abs ~ Fraction * Treat.Int, data = d13C.dat.perm, permutations = 1000, method = "bray", sqrt.dist = TRUE)
## Df SumOfSqs R2 F Pr(>F)
## Fraction 1 0.04509 0.13361 1.4072 0.1818
## Treat.Int 2 0.05214 0.15451 0.8136 0.6803
## Fraction:Treat.Int 2 0.04798 0.14218 0.7487 0.7762
## Residual 6 0.19225 0.56970
## Total 11 0.33746 1.00000
write.csv(perman.C, "output/perman.C.csv")
## Permanova nitrogen
d15N.dat.perm<- d15N.dat[!(d15N.dat$Fraction=="plank"),]
d15N.dat.perm$Threonine<-d15N.dat.perm$Threonine+3 # adding a constant to make positive values
d15N.dat.perm$Phenylalanine<-d15N.dat.perm$Phenylalanine+3 # adding a constant to make positive values
df.manova.N<-d15N.dat.perm[, c(11:23)] # remove factor columns
set.seed(138)
perman.N<-adonis2(df.manova.N~Fraction*Treat.Int, data=d15N.dat.perm, permutations=1000,
method="bray", sqrt.dist = TRUE)
perman.N
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1000
##
## adonis2(formula = df.manova.N ~ Fraction * Treat.Int, data = d15N.dat.perm, permutations = 1000, method = "bray", sqrt.dist = TRUE)
## Df SumOfSqs R2 F Pr(>F)
## Fraction 1 0.17919 0.18890 2.1474 0.01598 *
## Treat.Int 2 0.12370 0.13041 0.7412 0.84815
## Fraction:Treat.Int 2 0.14502 0.15288 0.8690 0.65634
## Residual 6 0.50066 0.52781
## Total 11 0.94857 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
write.csv(perman.N, "output/perman.N.csv")
Carbon by treatment
######## ######## ######## ########
######## ######## ######## ######## by treatment
# PCA dataframe
PCA.df.C<-d13C.dat[, c(7:8,11:23)]
PC.C<- prcomp(PCA.df.C[,c(-1:-2)], center = TRUE, scale= TRUE)
PC.C.summary<-summary(PC.C)
ev.C<-PC.C$sdev^2
newdat.C<-PC.C$x[,1:4] # 2 PCAs explain 81% of variance
#plot(PC, type="lines", main="PC.area eigenvalues")
## PC1 and PC2
trt.color<-c("lightskyblue", "darkgoldenrod1", "gray70", "mediumorchid")
PC.fig1.C.trt <- ggbiplot(PC.C, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.C[,1], ellipse = TRUE,
circle = FALSE) +
scale_color_manual(values=trt.color)+
theme_classic() +
scale_x_continuous(breaks=pretty_breaks(n=5))+
coord_cartesian(xlim = c(-8, 8), ylim=c(-4, 4))+
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=6, y=3.6, size=5, label= expression(paste(delta^{13},C[AA])))
print(PC.fig1.C.trt)
#ggsave("figures/carbon/PCA_d13C.trt.pdf", height=5, width=8, encod="MacRoman")
Nitrogen by treatment
# PCA dataframe
PCA.df.N<-d15N.dat[, c(7:8,11:23)]
PC.N<- prcomp(PCA.df.N[, c(-1:-2)], center = TRUE, scale= TRUE)
PC.N.summary<-summary(PC.N)
ev.N<-PC.N$sdev^2
newdat.N<-PC.N$x[,1:4] # 2 PCAs explain 74% of variance
#plot(PC, type="lines", main="PC.area eigenvalues")
######################## treatments
## PC1 and PC2
PC.fig2.N.trt <- ggbiplot(PC.N, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.N[,1], ellipse = TRUE,
circle = FALSE) +
scale_color_manual(values=trt.color)+
theme_classic() +
coord_cartesian(xlim = c(-8, 5), ylim=c(-6, 6)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=4, y=5.5, size=5, label= expression(paste(delta^{15},N[AA])))
print(PC.fig2.N.trt)
#ggsave("figures/nitrogen/PCA_d15N.trt.pdf", height=5, width=6, encod="MacRoman")
Carbon PCA by fraction (plankton, host, symbiont)
######## ######## ######## ########
######## ######## ######## ######## by fraction
frac.color<- c("coral", "seagreen3", "mediumorchid")
PC.fig3.C.frac <- ggbiplot(PC.C, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.C[,2], ellipse = TRUE,
circle = FALSE) +
scale_color_manual(values=frac.color)+
theme_classic() +
coord_cartesian(xlim = c(-8, 8), ylim=c(-4, 4)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=6, y=3.6, size=5, label= expression(paste(delta^{13},C[AA])))
print(PC.fig3.C.frac)
#ggsave("figures/carbon/PCA_d13C.frac.pdf", height=5, width=8, encod="MacRoman")
Nitrogen PCA by fraction (plankton, host, symbiont)
######################### fractions
## PC1 and PC2
PC.fig4.N.frac <- ggbiplot(PC.N, choices = 1:2, obs.scale = 1, var.scale = 1,
groups= PCA.df.N[,2], ellipse = TRUE,
circle = FALSE) +
scale_color_manual(values=frac.color)+
theme_classic() +
coord_cartesian(xlim = c(-8, 5), ylim=c(-6, 6)) +
theme(axis.ticks.length=unit(-0.25, "cm"), axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")), axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(legend.text=element_text(size=15)) +
theme(panel.background = element_rect(colour = "black", size=1))+
theme(legend.key = element_blank())+
theme(legend.direction = 'horizontal', legend.position = 'top') +theme(aspect.ratio=0.8) +
annotate("text", x=4, y=5.5, size=5, label= expression(paste(delta^{15},N[AA])))
print(PC.fig4.N.frac)
#ggsave("figures/nitrogen/PCA_d15N.frac.pdf", height=5, width=8, encod="MacRoman")
Break up C and N AA and do LDA for each. Includes all AA in the categories (Non-EAA/EAA) and (Trophic/Source).
# LDA dataframe
LDA.d13C<-d13C.dat[, c(8,11:23)] # (Just 'Fraction' tested here)
LDA.df.N<-d15N.dat[, c(7:8,11:23)]
# Split the data into training (80%) and test set (20%)
set.seed(123)
training.C.samples <- LDA.d13C$Fraction %>%
createDataPartition(p = 0.8, list = FALSE)
train.C.data <- LDA.d13C[training.C.samples, ]
test.C.data <- LDA.d13C[-training.C.samples, ]
#Normalize
# Estimate preprocessing parameters
preproc.param <- train.C.data %>%
preProcess(method = c("center", "scale"))
# Transform the data using the estimated parameters
train.transformed <- preproc.param %>% predict(train.C.data)
test.transformed <- preproc.param %>% predict(test.C.data)
# Fit the model (Just Fraction tested here)
model.C.frac <- lda(Fraction~., data = train.transformed)
# Make predictions
predictions <- model.C.frac %>% predict(test.transformed)
# Model accuracy
mean(predictions$class==test.transformed$Fraction) #0.5 accuracy
## [1] 0
### INFO ###
# LDA determines group means and computes, for each individual, the probability of belonging to the different groups. The individual is then affected to the group with the highest probability score.
# Output:
#Prior probabilities of groups: the proportion of training observations in each group. For example, there are 31% of the training observations in the setosa group
# Group means: group center of gravity. Shows the mean of each variable in each group.
# Coefficients of linear discriminants: Shows the linear combination of predictor variables that are used to form the LDA decision rule.
# Make predictions
predictions <- model.C.frac %>% predict(test.transformed)
names(predictions)
## [1] "class" "posterior" "x"
#The predict() function returns the following elements:
#class: predicted classes of observations.
#posterior: is a matrix whose columns are the groups, rows are the individuals and values are the posterior probability that the corresponding observation belongs to the groups.
#x: contains the linear discriminants, described above
# Predicted classes
head(predictions$class, 6)
## [1] plank host
## Levels: host symb plank
# Predicted probabilities of class membership.
head(predictions$posterior, 6)
## host symb plank
## 1 6.409439e-29 1.463557e-05 9.999854e-01
## 3 9.997676e-01 2.323526e-04 1.606492e-38
# Linear discriminants
head(predictions$x, 3)
## LD1 LD2
## 1 9.4802049 -4.229743
## 3 -0.8072902 1.182156
LDA.C.data <- cbind(train.transformed, predict(model.C.frac)$x)
mean(predictions$class==test.transformed$Fraction)
## [1] 0
# LDA d13C-AA Fraction
# for ellipses
library(ellipse)
dat_ell <- data.frame()
for(g in levels(LDA.C.data$Fraction)){
dat_ell <- rbind(dat_ell, cbind(as.data.frame(with(LDA.C.data[LDA.C.data$Fraction==g,], ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))),Fraction=g))
}
LDA.C.frac<-ggplot(LDA.C.data, aes(LD1, LD2)) +
geom_point(aes(color = Fraction))+
geom_path(data=dat_ell, aes(x=x,y=y,color=Fraction),size=0.5,linetype=2) +
scale_color_manual(values=frac.color)+
coord_cartesian(xlim = c(-4, 14), ylim=c(-4, 4)) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
panel.background=element_blank())
print(LDA.C.frac)
########### LDA Nitrogen
# LDA dataframe
LDA.df.N<-d15N.dat[, c(8,11:23)] # (Just 'Fraction' tested here)
# Split the data into training (80%) and test set (20%)
set.seed(123)
training.N.samples <- LDA.df.N$Fraction %>%
createDataPartition(p = 0.8, list = FALSE)
train.N.data <- LDA.df.N[training.N.samples, ]
test.N.data <- LDA.df.N[-training.N.samples, ]
preproc.N.param <- train.N.data %>%
preProcess(method = c("center", "scale"))
train.N.transformed <- preproc.N.param %>% predict(train.N.data)
test.N.transformed <- preproc.N.param %>% predict(test.N.data)
# Fit the model (Just Fraction tested here)
model.N.frac <- lda(Fraction~., data = train.N.transformed)
# Make predictions
predictions.N <- model.N.frac %>% predict(test.N.transformed)
# Model accuracy
mean(predictions.N$class==test.N.transformed$Fraction) #0.5 accuracy
## [1] 0.5
# Make predictions
predictions.N <- model.N.frac %>% predict(test.N.transformed)
names(predictions.N)
## [1] "class" "posterior" "x"
# Predicted classes
head(predictions.N$class, 6)
## [1] symb symb
## Levels: host symb plank
# Predicted probabilities of class membership.
head(predictions.N$posterior, 6)
## host symb plank
## 2 0.03037680 0.9696232 4.299412e-34
## 3 0.01346862 0.9865314 1.659611e-29
# Linear discriminants
head(predictions.N$x, 3)
## LD1 LD2
## 2 -0.70623181 -1.303294
## 3 0.02194191 -2.174022
LDA.N.data <- cbind(train.N.transformed, predict(model.N.frac)$x)
mean(predictions.N$class==test.N.transformed$Fraction)
## [1] 0.5
# LDA d13C-AA Fraction
# for ellipses
dat_ell.N <- data.frame()
for(g in levels(LDA.N.data$Fraction)){
dat_ell.N <- rbind(dat_ell.N, cbind(as.data.frame(with(LDA.N.data[LDA.N.data$Fraction==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))),Fraction=g))
}
LDA.N.frac<-ggplot(LDA.N.data, aes(LD1, LD2)) +
geom_point(aes(color = Fraction))+
scale_color_manual(values=frac.color) +
geom_path(data=dat_ell.N, aes(x=x,y=y,color=Fraction),size=0.5,linetype=2) +
coord_cartesian(xlim = c(-4, 14), ylim=c(-4, 4)) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
panel.background=element_blank())
print(LDA.N.frac)
plot_grid(LDA.C.frac, LDA.N.frac)
# LDA dataframe
# looking at Non-essential AA and Essential
# Non-EAA = Ala, Asp, Gly, Glu, Pro, Ser, Tyr
# EAA = Ile, Leu, Lys, Phe, Thr, Val
##########################
########################## Non-EAA C
# looking Non-EAA
# Non-EAA = Ala, Asp, Gly, Glu, Pro, Ser, Tyr
LDA.df.NEAAC<-LDA.d13C %>%
select(Fraction, Alanine, Aspartic.acid, Glycine, Glutamic.acid, Proline, Serine, Tyrosine)
# (Just 'Trophc' fractions here)
# Split the data into training (80%) and test set (20%)
set.seed(123)
training.C.NEAA.samples <- LDA.df.NEAAC$Fraction %>%
createDataPartition(p = 0.8, list = FALSE)
train.C.NEAA.data <- LDA.df.NEAAC[training.C.NEAA.samples, ]
test.C.NEAA.data <- LDA.df.NEAAC[-training.C.NEAA.samples, ]
preproc.C.NEAA.param <- train.C.NEAA.data %>%
preProcess(method = c("center", "scale"))
train.C.NEAA.transformed <- preproc.C.NEAA.param %>% predict(train.C.NEAA.data)
test.C.NEAA.transformed <- preproc.C.NEAA.param %>% predict(test.C.NEAA.data)
# Fit the model (Just Fraction tested here)
model.C.NEAA <- lda(Fraction~., data = train.C.NEAA.transformed)
# Make predictions
predictions.C.NEAA <- model.C.NEAA %>% predict(test.C.NEAA.transformed)
# Model accuracy
mean(predictions.C.NEAA$class==test.C.NEAA.transformed$Fraction) #0.5 accuracy
## [1] 0.5
# Make predictions
predictions.C.NEAA <- model.C.NEAA %>% predict(test.C.NEAA.transformed)
names(predictions.C.NEAA)
## [1] "class" "posterior" "x"
# Predicted classes
head(predictions.C.NEAA$class, 6)
## [1] host plank
## Levels: host symb plank
# Predicted probabilities of class membership.
head(predictions.C.NEAA$posterior, 6)
## host symb plank
## 1 1.0000000 0.00000e+00 2.125322e-205
## 3 0.4933633 5.17668e-21 5.066367e-01
# Linear discriminants
head(predictions.C.NEAA$x, 3)
## LD1 LD2
## 1 -38.259833 -8.713046
## 3 -2.273748 1.191146
LDA.C.NEAA.data <- cbind(train.C.NEAA.transformed, predict(model.C.NEAA)$x)
mean(predictions.C.NEAA$class==test.C.NEAA.transformed$Fraction)
## [1] 0.5
# LDA d13C-AA Fraction
# for ellipses
dat_ell.C.NEAA <- data.frame()
for(g in levels(LDA.C.data$Fraction)){
dat_ell.C.NEAA <- rbind(dat_ell.C.NEAA, cbind(as.data.frame(with(LDA.C.NEAA.data[LDA.C.NEAA.data$Fraction==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))),Fraction=g))
}
LDA.C.NEAA<-ggplot(LDA.C.NEAA.data, aes(LD1, LD2)) +
geom_point(aes(color = Fraction))+
scale_color_manual(values=frac.color)+
ggtitle(expression(paste(Non-EAA,~delta^{13}, C[AA], ""))) +
coord_cartesian(xlim = c(-10, 15), ylim=c(-5, 5)) +
geom_path(data=dat_ell.C.NEAA, aes(x=x,y=y,color=Fraction),size=0.5,linetype=2) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
panel.background=element_blank())
print(LDA.C.NEAA)
########## ########## ##########
########## EAA
LDA.df.EAAC<-LDA.d13C %>%
select(Fraction, Isoleucine, Leucine, Lysine, Phenylalanine, Threonine, Valine)
# (Just 'Trophc' fractions here)
# Split the data into training (80%) and test set (20%)
set.seed(123)
training.C.EAA.samples <- LDA.df.EAAC$Fraction %>%
createDataPartition(p = 0.8, list = FALSE)
train.C.EAA.data <- LDA.df.EAAC[training.C.EAA.samples, ]
test.C.EAA.data <- LDA.df.EAAC[-training.C.EAA.samples, ]
preproc.C.EAA.param <- train.C.EAA.data %>%
preProcess(method = c("center", "scale"))
train.C.EAA.transformed <- preproc.C.EAA.param %>% predict(train.C.EAA.data)
test.C.EAA.transformed <- preproc.C.EAA.param %>% predict(test.C.EAA.data)
# Fit the model (Just Fraction tested here)
model.C.EAA <- lda(Fraction~., data = train.C.EAA.transformed)
# Make predictions
predictions.C.EAA <- model.C.EAA %>% predict(test.C.EAA.transformed)
# Model accuracy
mean(predictions.C.EAA$class==test.C.EAA.transformed$Fraction) #0.5 accuracy
## [1] 0.5
# Make predictions
predictions.C.EAA <- model.C.EAA %>% predict(test.C.EAA.transformed)
names(predictions.C.EAA)
## [1] "class" "posterior" "x"
# Predicted classes
head(predictions.C.EAA$class, 6)
## [1] host plank
## Levels: host symb plank
# Predicted probabilities of class membership.
head(predictions.C.EAA$posterior, 6)
## host symb plank
## 1 1.000000e+00 7.460999e-37 1.177377e-37
## 3 7.099417e-05 5.910248e-44 9.999290e-01
# Linear discriminants
head(predictions.C.EAA$x, 3)
## LD1 LD2
## 1 11.49592 8.217632
## 3 14.52308 2.883417
LDA.C.EAA.data <- cbind(train.C.EAA.transformed, predict(model.C.EAA)$x)
mean(predictions.C.EAA$class==test.C.EAA.transformed$Fraction)
## [1] 0.5
# LDA d13C-AA Fraction
# for ellipses
dat_ell.C.EAA <- data.frame()
for(g in levels(LDA.C.data$Fraction)){
dat_ell.C.EAA <- rbind(dat_ell.C.EAA, cbind(as.data.frame(with(LDA.C.EAA.data[LDA.C.EAA.data$Fraction==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))),Fraction=g))
}
LDA.C.EAA<-ggplot(LDA.C.EAA.data, aes(LD1, LD2)) +
geom_point(aes(color = Fraction))+
scale_color_manual(values=frac.color)+
ggtitle(expression(paste(EAA,~delta^{13}, C[AA], ""))) +
coord_cartesian(xlim = c(-10, 15), ylim=c(-5, 5)) +
geom_path(data=dat_ell.C.EAA, aes(x=x,y=y,color=Fraction),size=0.5,linetype=2) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
panel.background=element_blank())
print(LDA.C.EAA)
plot_grid(LDA.C.NEAA, LDA.C.EAA)
# LDA dataframe
# looking at Trophic and Source
# Trophic = Ala, Asp, Glu, Ile, Leu, Val, Pro
# Source = Gly, Set, Ser, Phe, Thr, Tyr
########## Trophic AA
LDA.df.TrN<-LDA.df.N %>%
select(Fraction, Alanine, Aspartic.acid, Glutamic.acid, Isoleucine, Leucine, Valine, Proline)
# (Just 'Trophc' fractions here)
# Split the data into training (80%) and test set (20%)
set.seed(123)
training.N.Tr.samples <- LDA.df.TrN$Fraction %>%
createDataPartition(p = 0.8, list = FALSE)
train.N.Tr.data <- LDA.df.TrN[training.N.Tr.samples, ]
test.N.Tr.data <- LDA.df.TrN[-training.N.Tr.samples, ]
preproc.N.Tr.param <- train.N.Tr.data %>%
preProcess(method = c("center", "scale"))
train.N.Tr.transformed <- preproc.N.Tr.param %>% predict(train.N.Tr.data)
test.N.Tr.transformed <- preproc.N.Tr.param %>% predict(test.N.Tr.data)
# Fit the model (Just Fraction tested here)
model.N.Tr <- lda(Fraction~., data = train.N.Tr.transformed)
# Make predictions
predictions.N.Tr <- model.N.Tr %>% predict(test.N.Tr.transformed)
# Model accuracy
mean(predictions.N.Tr$class==test.N.Tr.transformed$Fraction) #0.5 accuracy
## [1] 0
# Make predictions
predictions.N.Tr <- model.N.Tr %>% predict(test.N.Tr.transformed)
names(predictions.N.Tr)
## [1] "class" "posterior" "x"
# Predicted classes
head(predictions.N.Tr$class, 6)
## [1] symb host
## Levels: host symb plank
# Predicted probabilities of class membership.
head(predictions.N.Tr$posterior, 6)
## host symb plank
## 2 1.049318e-08 0.99999999 1.761058e-105
## 3 9.722014e-01 0.02779858 1.375852e-88
# Linear discriminants
head(predictions.N.Tr$x, 3)
## LD1 LD2
## 2 -4.065532 -1.082257
## 3 -2.028678 1.279761
LDA.N.Tr.data <- cbind(train.N.Tr.transformed, predict(model.N.Tr)$x)
mean(predictions.N.Tr$class==test.N.Tr.transformed$Fraction)
## [1] 0
# LDA d13C-AA Fraction
# for ellipses
dat_ell.N.Tr <- data.frame()
for(g in levels(LDA.N.data$Fraction)){
dat_ell.N.Tr <- rbind(dat_ell.N.Tr, cbind(as.data.frame(with(LDA.N.Tr.data[LDA.N.Tr.data$Fraction==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))),Fraction=g))
}
LDA.N.Tr<-ggplot(LDA.N.Tr.data, aes(LD1, LD2)) +
geom_point(aes(color = Fraction))+
scale_color_manual(values=frac.color) +
ggtitle(expression(paste(Trophic,~delta^{15}, N[AA], ""))) +
coord_cartesian(xlim = c(-10, 20), ylim=c(-6, 6)) +
geom_path(data=dat_ell.N.Tr, aes(x=x,y=y,color=Fraction),size=0.5,linetype=2) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
panel.background=element_blank())
print(LDA.N.Tr)
#################
########################## Source AA N
# looking Source
# Source = Gly, Set, Ser, Phe, Thr, Tyr
LDA.df.SrN<-LDA.df.N %>%
select(Fraction, Glycine, Serine, Phenylalanine, Threonine, Tyrosine)
# (Just 'Trophc' fractions here)
# Split the data into training (80%) and test set (20%)
set.seed(123)
training.N.Sr.samples <- LDA.df.SrN$Fraction %>%
createDataPartition(p = 0.8, list = FALSE)
train.N.Sr.data <- LDA.df.SrN[training.N.Sr.samples, ]
test.N.Sr.data <- LDA.df.SrN[-training.N.Sr.samples, ]
preproc.N.Sr.param <- train.N.Sr.data %>%
preProcess(method = c("center", "scale"))
train.N.Sr.transformed <- preproc.N.Sr.param %>% predict(train.N.Sr.data)
test.N.Sr.transformed <- preproc.N.Sr.param %>% predict(test.N.Sr.data)
# Fit the model (Just Fraction tested here)
model.N.Sr <- lda(Fraction~., data = train.N.Sr.transformed)
# Make predictions
predictions.N.Sr <- model.N.Sr %>% predict(test.N.Sr.transformed)
# Model accuracy
mean(predictions.N.Sr$class==test.N.Sr.transformed$Fraction) #0.5 accuracy
## [1] 1
# Make predictions
predictions.N.Sr <- model.N.Sr %>% predict(test.N.Sr.transformed)
names(predictions.N.Sr)
## [1] "class" "posterior" "x"
# Predicted classes
head(predictions.N.Sr$class, 6)
## [1] host symb
## Levels: host symb plank
# Predicted probabilities of class membership.
head(predictions.N.Sr$posterior, 6)
## host symb plank
## 2 0.99694628 0.003053716 2.670533e-22
## 3 0.03919395 0.960806047 1.787077e-45
# Linear discriminants
head(predictions.N.Sr$x, 3)
## LD1 LD2
## 2 1.577231 -0.1241904
## 3 -2.660558 0.2897541
LDA.N.Sr.data <- cbind(train.N.Sr.transformed, predict(model.N.Sr)$x)
mean(predictions.N.Sr$class==test.N.Sr.transformed$Fraction)
## [1] 1
# LDA d13C-AA Fraction
# for ellipses
dat_ell.N.Sr <- data.frame()
for(g in levels(LDA.N.data$Fraction)){
dat_ell.N.Sr <- rbind(dat_ell.N.Sr, cbind(as.data.frame(with(LDA.N.Sr.data[LDA.N.Sr.data$Fraction==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))),Fraction=g))
}
LDA.N.Sr<-ggplot(LDA.N.Sr.data, aes(LD1, LD2)) +
geom_point(aes(color = Fraction))+
scale_color_manual(values=frac.color)+
ggtitle(expression(paste(Source,~delta^{15}, N[AA], ""))) +
coord_cartesian(xlim = c(-10, 20), ylim=c(-6, 6)) +
geom_path(data=dat_ell.N.Sr, aes(x=x,y=y,color=Fraction),size=0.5,linetype=2) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
panel.background=element_blank())
print(LDA.N.Sr)
plot_grid(LDA.N.Tr, LDA.N.Sr)
Run models looking for effects of Fraction or the Treatment-Interaction (feeding/light).
d13C.dat2<-d13C.dat[!(d13C.dat$Fraction=="plank"),] #remove plankton for now
d13C.host<-d13C.dat[(d13C.dat$Fraction=="host"),] # just host
d13C.symb<-d13C.dat[(d13C.dat$Fraction=="symb"),] # just symbiont
for(i in c(11:23)){
Y=d13C.dat2[,i]
mod<-aov(Y~Fraction+Treat.Int, data=d13C.dat2)
print(anova(mod))
print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d13C.dat2)[i], cex.axis=0.5, cex.lab=0.5)
}
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 7.9280 7.9280 4.8489 0.05881 .
## Treat.Int 2 2.3861 1.1931 0.7297 0.51157
## Residuals 8 13.0802 1.6350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -1.625629 -3.328026 0.07676902 0.0588088
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -1.0214364 -3.605031 1.562158 0.5238902
## D-F-L-NF -0.1756140 -2.759208 2.407980 0.9794709
## D-F-L-F 0.8458224 -1.737772 3.429417 0.6346704
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 43.514 43.514 16.281 0.003762 **
## Treat.Int 2 38.910 19.455 7.279 0.015818 *
## Residuals 8 21.382 2.673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -3.808515 -5.985115 -1.631916 0.0037621
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -2.323060 -5.626314 0.9801931 0.1719320
## D-F-L-NF 2.085575 -1.217679 5.3888282 0.2286943
## D-F-L-F 4.408635 1.105382 7.7118885 0.0126616
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 2.197 2.1971 0.4464 0.5228
## Treat.Int 2 5.526 2.7628 0.5614 0.5914
## Residuals 8 39.372 4.9215
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.8557828 -2.097784 3.80935 0.5228408
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -1.6618056 -6.144201 2.820590 0.5632924
## D-F-L-NF -0.8014268 -5.283822 3.680969 0.8682426
## D-F-L-F 0.8603788 -3.622017 5.342774 0.8501124
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.5098 0.5098 0.1910 0.6736
## Treat.Int 2 13.3334 6.6667 2.4977 0.1436
## Residuals 8 21.3529 2.6691
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.4122222 -1.762895 2.58734 0.6736481
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -1.363812 -4.6648158 1.937192 0.4961333
## D-F-L-NF 1.216790 -2.0842139 4.517794 0.5667464
## D-F-L-F 2.580602 -0.7204022 5.881606 0.1247308
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 3.0549 3.0549 1.2730 0.2919
## Treat.Int 2 4.4772 2.2386 0.9328 0.4324
## Residuals 8 19.1986 2.3998
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -1.009111 -3.071587 1.053365 0.291906
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.5855417 -3.715598 2.544515 0.8569269
## D-F-L-NF 0.8996250 -2.230431 4.029681 0.7011505
## D-F-L-F 1.4851667 -1.644890 4.615223 0.4064527
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.1806 0.18060 0.1457 0.7126
## Treat.Int 2 1.8191 0.90953 0.7340 0.5097
## Residuals 8 9.9129 1.23912
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.2453535 -1.727379 1.236672 0.7125759
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.5112879 -2.760441 1.737865 0.7978109
## D-F-L-NF 0.4415530 -1.807600 2.690706 0.8439105
## D-F-L-F 0.9528409 -1.296312 3.201994 0.4800982
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 2.301 2.3006 0.5090 0.4958
## Treat.Int 2 0.536 0.2681 0.0593 0.9428
## Residuals 8 36.157 4.5196
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.8757071 -3.706106 1.954692 0.4958429
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.1529924 -4.448466 4.142481 0.9943098
## D-F-L-NF 0.3519318 -3.943541 4.647405 0.9703487
## D-F-L-F 0.5049242 -3.790549 4.800398 0.9401608
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 10.0959 10.0959 5.0226 0.05533 .
## Treat.Int 2 1.9626 0.9813 0.4882 0.63089
## Residuals 8 16.0806 2.0101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -1.834472 -3.722052 0.05310792 0.0553284
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.9262500 -3.790881 1.938381 0.6413964
## D-F-L-NF -0.1589583 -3.023590 2.705673 0.9862591
## D-F-L-F 0.7672917 -2.097340 3.631923 0.7333288
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.9366 1.93656 0.8704 0.3781
## Treat.Int 2 1.1095 0.55476 0.2494 0.7851
## Residuals 8 17.7984 2.22480
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.8034428 -1.182399 2.789285 0.3781308
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.3559343 -3.369690 2.657821 0.9396161
## D-F-L-NF -0.7445833 -3.758339 2.269172 0.7668729
## D-F-L-F -0.3886490 -3.402404 2.625106 0.9285371
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 15.4184 15.4184 15.5000 0.004314 **
## Treat.Int 2 7.1774 3.5887 3.6077 0.076423 .
## Residuals 8 7.9579 0.9947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -2.267039 -3.594905 -0.9391742 0.0043141
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF -0.3928728 -2.4080690 1.622323 0.8458544
## D-F-L-NF 1.4084868 -0.6067093 3.423683 0.1749601
## D-F-L-F 1.8013597 -0.2138365 3.816556 0.0781114
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.4717 0.47171 0.3402 0.5758
## Treat.Int 2 1.7167 0.85836 0.6190 0.5624
## Residuals 8 11.0933 1.38666
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.3965321 -1.964311 1.171246 0.5757947
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF 0.805110680 -1.574183 3.184404 0.6164040
## D-F-L-NF 0.799570313 -1.579723 3.178864 0.6202649
## D-F-L-F -0.005540367 -2.384834 2.373753 0.9999756
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.4274 0.42736 0.2195 0.6519
## Treat.Int 2 3.7940 1.89702 0.9743 0.4181
## Residuals 8 15.5772 1.94716
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host 0.3774306 -1.480373 2.235234 0.6519364
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF 1.1068359 -1.712606 3.926277 0.5282887
## D-F-L-NF 1.2633138 -1.556128 4.082755 0.4438334
## D-F-L-F 0.1564779 -2.662964 2.975919 0.9862544
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.7150 0.71500 0.4577 0.5178
## Treat.Int 2 0.0828 0.04142 0.0265 0.9739
## Residuals 8 12.4980 1.56225
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Fraction + Treat.Int, data = d13C.dat2)
##
## $Fraction
## diff lwr upr p adj
## symb-host -0.4881944 -2.152277 1.175888 0.5177911
##
## $Treat.Int
## diff lwr upr p adj
## L-F-L-NF 0.14959239 -2.375854 2.675039 0.9843613
## D-F-L-NF -0.04470109 -2.570148 2.480746 0.9985909
## D-F-L-F -0.19429348 -2.719740 2.331153 0.9737967
# Almost Fraction effect for Alanine, Proline
# Fraction effect for: Glycine, Glutamic acid
# Treatment effect for: Glycine
# Almost Treatment effect: Glutamic Acid
############ just host
for(i in c(11:23)){
Y=d13C.host[,i]
mod<-aov(Y~Treat.Int, data=d13C.host)
print(anova(mod))
#print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d13C.host)[i], cex.axis=0.5, cex.lab=0.5)
}
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.0932 0.04660 0.0567 0.9459
## Residuals 3 2.4654 0.82179
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 10.8858 5.4429 7.9309 0.06343 .
## Residuals 3 2.0589 0.6863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 6.8484 3.4242 0.4379 0.681
## Residuals 3 23.4580 7.8193
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 6.8177 3.4088 1.3167 0.3886
## Residuals 3 7.7669 2.5890
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.9241 0.46205 0.1674 0.8533
## Residuals 3 8.2826 2.76086
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.5210 0.76052 0.5506 0.6256
## Residuals 3 4.1441 1.38137
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.2875 0.1438 0.0418 0.9596
## Residuals 3 10.3123 3.4374
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.21445 0.60723 2.347 0.2435
## Residuals 3 0.77618 0.25873
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.1362 1.06810 1.37 0.3778
## Residuals 3 2.3388 0.77962
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 4.3768 2.1884 1.5503 0.3448
## Residuals 3 4.2349 1.4116
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.9503 1.4751 0.9893 0.4677
## Residuals 3 4.4731 1.4910
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 5.4088 2.7044 1.4422 0.364
## Residuals 3 5.6255 1.8752
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.3085 1.15427 3.3645 0.1712
## Residuals 3 1.0292 0.34307
# If running only "Treat.Int" no effects accept p=0.06 for glycine
# If running "Light.Trt+Feed.Trt" then p=0.03 for glycine
############ just symbiont
for(i in c(11:23)){
Y=d13C.symb[,i]
mod<-aov(Y~Treat.Int, data=d13C.symb)
print(anova(mod))
#print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d13C.symb)[i], cex.axis=0.5, cex.lab=0.5)
}
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 3.7832 1.8916 0.6219 0.5943
## Residuals 3 9.1245 3.0415
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 30.814 15.4072 2.7958 0.2063
## Residuals 3 16.533 5.5109
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.3166 1.1583 0.2831 0.7716
## Residuals 3 12.2743 4.0914
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 17.036 8.5178 8.3344 0.05957 .
## Residuals 3 3.066 1.0220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 4.4949 2.2475 0.676 0.5723
## Residuals 3 9.9741 3.3247
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.4550 0.2275 0.1216 0.8896
## Residuals 3 5.6118 1.8706
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.8795 0.4397 0.0523 0.9499
## Residuals 3 25.2135 8.4045
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.2539 0.6270 0.1271 0.8851
## Residuals 3 14.7986 4.9329
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 7.4714 3.7357 1.6099 0.335
## Residuals 3 6.9615 2.3205
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 2.9467 1.4733 1.2357 0.406
## Residuals 3 3.5769 1.1923
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 0.0713 0.03566 0.0201 0.9802
## Residuals 3 5.3153 1.77176
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 4.3532 2.1766 1.6391 0.3303
## Residuals 3 3.9837 1.3279
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Treat.Int 2 1.8423 0.92114 0.3734 0.7165
## Residuals 3 7.4008 2.46694
# If running only "Treat.Int" no effects
# If running "Light.Trt+Feed.Trt" then p=0.03 for serine
-New dataframe (long format) here to make figures. Same data as above.
###########
###########
# CSAA.dat long
d13C.dat.long<-read.csv("data/d13C.CSIA.long.csv")
#str(d13C.dat.long)
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Methionine"),]
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Norleucine"),]
d13C.dat.long<-d13C.dat.long[!(d13C.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d13C.dat.long$AA.short<-mapvalues(d13C.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glu", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d13C.dat.long$AA.short<-factor(d13C.dat.long$AA.short, levels=c("Ala","Asp", "Gly", "Glu", "Pro", "Ser", "Tyr", "Ile", "Leu", "Lys", "Phe", "Thr", "Val"))
d13C.dat.long$Treat.Int<-factor(d13C.dat.long$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
# looking at average essential and non-essential AA
d13C.dat.long$AA.cat<-ifelse(d13C.dat.long$AA.short=="Ala" |d13C.dat.long$AA.short=="Asp" |
d13C.dat.long$AA.short=="Gly" | d13C.dat.long$AA.short=="Glu" |
d13C.dat.long$AA.short=="Pro" | d13C.dat.long$AA.short=="Ser" |
d13C.dat.long$AA.short=="Tyr", "Non-EAA", "EAA")
dfC<-d13C.dat.long
##################
# all essentail and non-essential AA
AA.means<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC, mean, na.rm=TRUE); AA.means
AA.sd<-aggregate(d13C.value~AA.cat+Treat.Int+Fraction, data=dfC, sd, na.rm=TRUE)
colnames(AA.sd)[4]="SD"
AA.means<-cbind(AA.means, AA.sd[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
#################
#################
# d13C by fraction and treatments
df.mean<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, mean, na.rm=TRUE)
df.n<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, length)
df.SD<-aggregate(d13C.value~AA.short+Fraction+Treat.Int, data=dfC, sd, na.rm=TRUE)
colnames(df.SD)[4]="SD"
df.mean<-cbind(df.mean, df.SD[4])
# replace NA for plankton SD as 0
df.mean$SD[is.na(df.mean$SD)] <- 0
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
df.mean$Treat.Int<-factor(df.mean$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
## d13C just by fraction
df.mean.frac<-aggregate(d13C.value~AA.short+Fraction, data=dfC, mean, na.rm=TRUE)
df.n.frac<-aggregate(d13C.value~AA.short+Fraction, data=dfC, length)
df.SE.frac<-aggregate(d13C.value~AA.short+Fraction, data=dfC, std.error, na.rm=TRUE)
colnames(df.SE.frac)[3]="SE"
df.SE.frac[is.na(df.SE.frac)] <- 0
df.mean.frac<-cbind(df.mean.frac, df.SE.frac[3])
df.mean.frac$Fraction<-factor(df.mean.frac$Fraction, levels=c("host", "symb", "plank"))
write.csv(df.mean.frac, "output/d13C.mean.frac.csv")
######## Figures
Fig.formatting<-(theme_classic()) +
theme(text=element_text(size=10),
axis.line=element_blank(),
legend.text.align = 0,
legend.text=element_text(size=10),
#legend.title = element_blank(),
panel.border = element_rect(fill=NA, colour = "black", size=1),
aspect.ratio=1,
axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=10),
axis.text.x=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=8)) +
theme(legend.key.size = unit(0.4, "cm")) +
theme(aspect.ratio=1) +
theme(panel.spacing=unit(c(0, 0, 0, 0), "cm"))
#######
pd <- position_dodge(0.5) #offset for error bars
d13C.CSIA_frac.trt<-ggplot(df.mean, aes(x=AA.short, y=d13C.value)) +
geom_point(size=2, position=pd, aes(shape=Treat.Int, color=Fraction, group=Treat.Int)) +
geom_errorbar(aes(ymin=d13C.value-SD, ymax=d13C.value+SD, color=Fraction, group=Treat.Int),
size=.5, width=0, position=pd) +
ggtitle(expression(paste(delta^{13}, C[AA], " by biological fraction and treatment"))) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=0, color="gray40") +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
scale_color_manual(values=c(frac.color)) +
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
Fig.formatting
print(d13C.CSIA_frac.trt)
#ggsave("figures/carbon/d13C.CSIA_frac.trt.pdf", height=5, width=8, encod="MacRoman")
##################
# all d13C amino acids
df.mean2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, mean, na.rm=TRUE)
df.SE2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, na.rm=TRUE, std.error)
df.n2<-aggregate(d13C.value~AA.short+Treat.Int, data=dfC, length)
df.SE2[is.na(df.SE2)] <- 0
colnames(df.SE2)[3]="SE"
df.mean2<-cbind(df.mean2, df.SE2[3])
pd <- position_dodge(0.5) #offset for error bars
d13C.CSIA_Trt.alone<-ggplot(df.mean2, aes(x=AA.short, y=d13C.value, group=Treat.Int)) +
geom_errorbar(aes(ymin=d13C.value-SE, ymax=d13C.value+SE, color=Treat.Int), size=.5, width=0, position=pd) +
geom_point(aes(color=Treat.Int, shape=Treat.Int), size=2, position=pd) +
scale_shape_manual(values=c(19,19,19,3))+
scale_color_manual(values=trt.color)+
ggtitle(expression(paste(delta^{13}, C[AA], " by treatment"))) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=0, color="gray40") +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
Fig.formatting
print(d13C.CSIA_Trt.alone)
#ggsave("figures/carbon/d13C.CSIA_Trt.alone.pdf", height=5, width=8, encod="MacRoman")
# all AA pooled by fraction
df.mean3<-aggregate(d13C.value~AA.short+Fraction, data=dfC, mean, na.rm=TRUE)
df.SE3<-aggregate(d13C.value~AA.short+Fraction, data=dfC, na.rm=TRUE, std.error)
df.n3<-aggregate(d13C.value~AA.short+Fraction, data=dfC, length)
df.SE3[is.na(df.SE3)] <- 0
colnames(df.SE3)[3]="SE"
df.mean3<-cbind(df.mean3, df.SE3[3])
df.mean3$Fraction<-factor(df.mean3$Fraction, levels=c("host", "symb", "plank"))
pd <- position_dodge(0.5) #offset for error bars
d13C.CSIA_Fraction<- ggplot(df.mean3, aes(x=AA.short, y=d13C.value)) +
geom_errorbar(aes(ymin=d13C.value-SE, ymax=d13C.value+SE, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=2, position=pd, aes(color=Fraction, shape=Fraction)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=0, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=0, color="gray40") +
ggtitle(expression(paste(delta^{13}, C[AA], " by biological fraction"))) +
coord_cartesian(ylim=c(-35, 0)) +
xlab("Amino Acids") +
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
scale_color_manual(values=frac.color) +
scale_shape_manual(values=c(19,19,3))+
Fig.formatting
print(d13C.CSIA_Fraction)
#ggsave("figures/carbon/d13C.CSIA_Fraction.pdf", height=5, width=8, encod="MacRoman")
Overall we see:
- Fraction effect for: Leucine, Proline, Aspartic Acid, Glutamic Acid, Tyrosine.
- Treatment effect for: Leucine
d15N.dat2<-d15N.dat[!(d15N.dat$Fraction=="plank"),] #remove plankton for now
for(i in c(11:23)){
Y=d15N.dat2[,i]
mod<-aov(Y~Fraction+Treat.Int, data=d15N.dat2)
print(anova(mod), cex=0.5)
#print(TukeyHSD(mod))
plot(allEffects(mod), ylab=colnames(d15N.dat2)[i], cex.axis=0.5)
}
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 8.1676 8.1676 3.3922 0.1028
## Treat.Int 2 1.8579 0.9290 0.3858 0.6919
## Residuals 8 19.2618 2.4077
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 4.2262 4.2262 1.6503 0.2349
## Treat.Int 2 1.5693 0.7846 0.3064 0.7444
## Residuals 8 20.4868 2.5609
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 13.327 13.3269 2.6576 0.1417
## Treat.Int 2 3.612 1.8061 0.3602 0.7083
## Residuals 8 40.117 5.0147
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.0006 0.00062 0.0003 0.9860
## Treat.Int 2 1.6612 0.83058 0.4397 0.6589
## Residuals 8 15.1127 1.88908
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.2248 0.22482 0.2201 0.6515
## Treat.Int 2 0.0415 0.02076 0.0203 0.9799
## Residuals 8 8.1699 1.02123
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 9.9952 9.9952 19.9043 0.002107 **
## Treat.Int 2 3.8088 1.9044 3.7924 0.069430 .
## Residuals 8 4.0173 0.5022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.3383 0.3383 0.0908 0.7708
## Treat.Int 2 3.8576 1.9288 0.5178 0.6145
## Residuals 8 29.8002 3.7250
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 22.9354 22.9354 28.9356 0.0006623 ***
## Treat.Int 2 4.1174 2.0587 2.5973 0.1351371
## Residuals 8 6.3411 0.7926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 3.3328 3.3328 6.3165 0.03619 *
## Treat.Int 2 1.4198 0.7099 1.3455 0.31354
## Residuals 8 4.2210 0.5276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.1145 1.11452 2.3608 0.1630
## Treat.Int 2 0.9348 0.46741 0.9901 0.4129
## Residuals 8 3.7768 0.47210
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 1.4002 1.40019 0.5355 0.4852
## Treat.Int 2 0.8007 0.40036 0.1531 0.8605
## Residuals 8 20.9164 2.61455
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 16.163 16.163 8.9499 0.01729 *
## Treat.Int 2 0.128 0.064 0.0354 0.96532
## Residuals 8 14.448 1.806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## Fraction 1 0.1051 0.10513 0.0799 0.7846
## Treat.Int 2 0.2452 0.12259 0.0931 0.9120
## Residuals 8 10.5298 1.31623
# Fraction effect for: Leucine, Proline, Aspartic Acid, Glutamic Acid, Tyrosine
# Treatment effect for: Leucine
###########
###########
# d15N.CSIA.dat long
d15N.dat.long<-read.csv("data/d15N.CSIA.long.csv")
#str(d15N.dat.long)
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Methionine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Norleucine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d15N.dat.long$AA.short<-mapvalues(d15N.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glu", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d15N.dat.long$AA.short<-factor(d15N.dat.long$AA.short, levels=c("Ala","Asp", "Glu", "Ile", "Leu", "Pro", "Val", "Gly", "Lys", "Ser", "Phe", "Thr", "Tyr"))
# looking at average Trophic and Source
d15N.dat.long$AA.cat<-ifelse(d15N.dat.long$AA.short=="Asp" | d15N.dat.long$AA.short=="Glu" |
d15N.dat.long$AA.short=="Ala" | d15N.dat.long$AA.short=="Ile" |
d15N.dat.long$AA.short=="Leu" | d15N.dat.long$AA.short=="Val" |
d15N.dat.long$AA.short=="Pro", "Troph", "Source")
d15N.dat.long$Treat.Int<-factor(d15N.dat.long$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
dfN<-d15N.dat.long
######## Figures
pd <- position_dodge(0.5) #offset for error bars
df.mean<-aggregate(d15N.value~AA.short+Fraction, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+Fraction, data=dfN, length)
df.SE<-aggregate(d15N.value~AA.short+Fraction, data=dfN, std.error, na.rm=TRUE)
df.SE[is.na(df.SE)] <- 0
colnames(df.SE)[3]="SE"
df.mean<-cbind(df.mean, df.SE[3])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
write.csv(df.mean, "output/d15N.frac.meanse.csv")
Fig.formatting<-(theme_classic()) +
theme(text=element_text(size=10),
axis.line=element_blank(),
legend.text.align = 0,
legend.text=element_text(size=10),
#legend.title = element_blank(),
panel.border = element_rect(fill=NA, colour = "black", size=1),
aspect.ratio=1,
axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=10),
axis.text.x=element_text(
margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"), colour="black", size=8)) +
theme(legend.key.size = unit(0.4, "cm")) +
theme(aspect.ratio=1) +
theme(panel.spacing=unit(c(0, 0, 0, 0), "cm"))
######
# all AA pooled by fraction
d15N.CSIA_Fraction<-ggplot(df.mean, aes(x=AA.short, y=d15N.value)) +
geom_errorbar(aes(ymin=d15N.value-SE, ymax=d15N.value+SE, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=2, position=pd, aes(color=Fraction, shape=Fraction)) +
scale_shape_manual(values=c(19,19,3))+
ggtitle(expression(paste(delta^{15}, N[AA], " by biological fraction"))) +
coord_cartesian(ylim=c(-5, 15)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
scale_color_manual(values=frac.color)+
Fig.formatting
print(d15N.CSIA_Fraction)
#ggsave("figures/nitrogen/d15N.CSIA_Fraction.pdf", height=5, width=8, encod="MacRoman")
#################
df.mean<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, length)
df.SD<-aggregate(d15N.value~AA.short+Fraction+Treat.Int, data=dfN, sd, na.rm=TRUE)
colnames(df.SD)[4]="SD"
df.SD[is.na(df.SD)] <- 0
df.mean<-cbind(df.mean, df.SD[4])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
df.mean$Treat.Int<-factor(df.mean$Treat.Int, levels=c("L-NF", "L-F", "D-F", "Plank"))
d15N.CSIA_frac.trt<-ggplot(df.mean, aes(x=AA.short, y=d15N.value)) +
geom_point(size=2, aes(shape=Treat.Int, color=Fraction, group=Treat.Int), position=pd) +
geom_errorbar(aes(ymin=d15N.value-SD, ymax=d15N.value+SD, group=Treat.Int, color=Fraction),
size=.5, width=0, position=pd) +
ggtitle(expression(paste(delta^{15}, N[AA], " by biological fraction, treatment"))) +
coord_cartesian(ylim=c(-5, 15)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
scale_color_manual(values=frac.color) +
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
Fig.formatting
print(d15N.CSIA_frac.trt)
#ggsave("figures/nitrogen/d15N.CSIA_frac.trt.pdf", height=5, width=8, encod="MacRoman")
##################
# all d15N amino acids
df.mean2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.n2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, length)
df.SE2<-aggregate(d15N.value~AA.short+Treat.Int, data=dfN, na.rm=TRUE, std.error)
df.SE2[is.na(df.SE2)] <- 0
colnames(df.SE2)[3]="SE"
df.mean2<-cbind(df.mean2, df.SE2[3])
d15N.CSIA_Trt.alone<-ggplot(df.mean2, aes(x=AA.short, y=d15N.value, group=Treat.Int)) +
geom_errorbar(aes(ymin=d15N.value-SE, ymax=d15N.value+SE, color=Treat.Int), size=.5, width=0, position=pd) +
geom_point(aes(color=Treat.Int, shape=Treat.Int), size=2, position=pd) +
ggtitle(expression(paste(delta^{15}, N[AA], " by treatment"))) +
coord_cartesian(ylim=c(-5, 15)) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Trophic-AA", x=4, y=15, color="gray40") +
annotate(geom="text", label="Source-AA", x=10, y=15, color="gray40") +
xlab("Amino Acids") +
scale_color_manual(values=trt.color) +
scale_shape_manual(values=c(19,19,19,3))+
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
Fig.formatting
print(d15N.CSIA_Trt.alone)
#ggsave("figures/nitrogen/d15N.CSIA_Trt.alone.pdf", height=5, width=8, encod="MacRoman")
##################
# all source and trophic) AA
AA.means<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN, mean, na.rm=TRUE)
AA.n<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN, length)
AA.se<-aggregate(d15N.value~AA.cat+Treat.Int+Fraction, data=dfN, std.error, na.rm=TRUE)
colnames(AA.se)[4]="SE"
AA.means<-cbind(AA.means, AA.se[4])
AA.means$Fraction<-factor(AA.means$Fraction, levels=c("host", "symb", "plank"))
AA.means$Treat.Int<-factor(AA.means$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
ggplot(AA.means, aes(x=Treat.Int, y=d15N.value, shape=AA.cat, color=Fraction)) +
geom_errorbar(aes(ymin=d15N.value-SE, ymax=d15N.value+SE, shape=AA.cat, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(aes(shape=AA.cat, color=Fraction), size=3, position=pd) +
ggtitle(expression(paste(delta^{15}, N[AA], " by biological fraction, Tr/So-AA"))) +
coord_cartesian(ylim=c(0, 12)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
scale_color_manual(values=c("coral", "springgreen3", "skyblue3")) +
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
Fig.formatting
#ggsave("figures/nitrogen/d15N.CSIA_TrSo.pdf", height=5, width=8, encod="MacRoman")
Trophic position using trophic AA gultamic acid (Glu) and source amino acid phenylalanine (Phe), following Chikaraishi et al. 2009
\(Trophic~position~(TP){_C}{_S}{_I}{_A}= [(δ{^1}{^5}N{_T}{_r}{_p}~-~δ{^1}{^5}N{_S}{_r}{_c})-B/~TDF{_A}{_A} +1\)
###
# scatter of glutamic.acid vs. phenylalanine
### ### ###
### ### ### trophic position using trophic (Glu) and source (Phe) AA, Chikaraishi et al. 2009
d15N.dat # dataframe here
# glu = trophic AA (changing with food, show enrichment realtive to source
# phe = source AA (showlittle change with increasing trophic position, reflect d15N baseline)
# beta = 3.4 (difference in d15N values among trophic and source AAs in primary producers, @ TP=1)
# TDFAA = trophic discrimination factor: mean 15N enrichment of >=1 trophic vs. source AA per trophic level
d15N.dat<-d15N.dat %>% mutate(TP = ((Glutamic.acid - Phenylalanine - 3.4)/7.6) +1)
# run a model
TP.df<-d15N.dat
TP.df<-TP.df[!(TP.df$Fraction=="plank"),] # remove plankton
anova(lm(TP~Fraction+Treat.Int, data=TP.df)) # no difference
df.mean<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, mean, na.rm=TRUE)
df.n<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, length)
df.SD<-aggregate(TP~Treat.Int+Fraction, data=d15N.dat, sd, na.rm=TRUE)
colnames(df.SD)[3]="SD"
df.mean<-cbind(df.mean, df.SD[3])
df.mean$Fraction<-factor(df.mean$Fraction, levels=c("host", "symb", "plank"))
df.mean$Treat.Int<-factor(df.mean$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
TP<-ggplot(df.mean, aes(x=Treat.Int, y=TP)) +
geom_errorbar(aes(ymin=TP-SD, ymax=TP+SD, color=Fraction),
size=.5, width=0, position=pd) +
geom_point(size=2, position=pd, aes(color=Fraction, shape=Fraction)) +
ggtitle("Chikaraishi trophic position") +
coord_cartesian(ylim=c(0, 3)) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed", "Plankton")) +
ylab(expression(paste("TP "~delta^{15}, N[Glu-Phe], " (\u2030, Air)"))) +
scale_shape_manual(values=c(19,19,3))+
scale_color_manual(values=frac.color) +
Fig.formatting
#print(TP)
#ggsave("figures/nitrogen/TP.glu.phe.pdf", height=5, width=8, encod="MacRoman")
Calculate Sum-V, McCarthy et al. 2007. The sum-V parameter is a proxy for total heterotrophic resynthesis. It is defined as the average deviation in the d15N values of the trophic amino acids Ala, Asp, Glu, Ile, Leu, and Pro.
###########
###########
# d15N.CSIA.dat long
d15N.dat.long<-read.csv("data/d15N.CSIA.long.csv")
#str(d15N.dat.long)
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Methionine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Norleucine"),]
d15N.dat.long<-d15N.dat.long[!(d15N.dat.long$Amino.acid=="Aminoadipic Acid"),] # remove unwanted data
d15N.dat.long$AA.short<-mapvalues(d15N.dat.long$Amino.acid, from =c("Alanine", "Aspartic acid", "Glutamic acid", "Glycine", "Isoleucine", "Leucine", "Lysine", "Phenylalanine", "Proline", "Serine", "Threonine", "Tyrosine", "Valine"), to = c("Ala", "Asp", "Glu", "Gly", "Ile", "Leu", "Lys", "Phe", "Pro", "Ser", "Thr", "Tyr", "Val"))
d15N.dat.long$AA.short<-factor(d15N.dat.long$AA.short, levels=c("Ala","Asp", "Glu", "Ile", "Leu", "Pro", "Val", "Gly", "Lys", "Ser", "Phe", "Thr", "Tyr"))
# looking at average Trophic and Source
d15N.dat.long$AA.cat<-ifelse(d15N.dat.long$AA.short=="Asp" | d15N.dat.long$AA.short=="Glu" |
d15N.dat.long$AA.short=="Ala" | d15N.dat.long$AA.short=="Ile" |
d15N.dat.long$AA.short=="Leu" | d15N.dat.long$AA.short=="Val" |
d15N.dat.long$AA.short=="Pro", "Troph", "Source")
d15N.dat.long$Treat.Int<-factor(d15N.dat.long$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
dfN<-d15N.dat.long
###########################
###########################
###########################
# dfN is dataframe
sV.df<-dfN[!(dfN$AA.short=="Thr"), ] # not good source, remove here
# make dataframe for AA, that with su deviance = sum-V
sV.df<-sV.df[c(sV.df$AA.short=="Ala" | sV.df$AA.short=="Glu" | sV.df$AA.short=="Asp" |
sV.df$AA.short=="Ile" | sV.df$AA.short=="Leu" | sV.df$AA.short=="Pro"),]
#write.csv(sV.df, "sumV.csv")
sumVdf<-read.csv("data/sumV.csv")
sumVdf$Fraction<-factor(sumVdf$Fraction, levels=c("host", "symb", "plank"))
sumVdf$Treat.Int<-factor(sumVdf$Treat.Int, levels=c("L-NF", "L-F", "D-F", "plank"))
######## model
sumVdf.mod<-sumVdf[!(sumVdf$Fraction=="plank"),] # remove plankton
anova(lm(sumV~Fraction + Treat.Int, data=sumVdf.mod)) # no difference
########
sumVdf.mean<-aggregate(sumV~AA.short+AA.cat+Fraction+Treat.Int, data=sumVdf, mean, na.rm=TRUE)
sumVdf.n<-aggregate(sumV~AA.short+AA.cat+Fraction+Treat.Int, data=sumVdf, length)
sumVdf.sd<-aggregate(sumV~AA.short+AA.cat+Fraction+Treat.Int, data=sumVdf, sd, na.rm=TRUE)
sumVdf<-cbind(sumVdf.mean, sumVdf.sd[5])
colnames(sumVdf)[5]<-"mean.sumV"
colnames(sumVdf)[6]="SD"
sumVdf[is.na(sumVdf)] <- 0
pd <- position_dodge(0.7) #offset for error bars and columns
sumV<-ggplot(sumVdf, aes(x=Treat.Int, y=mean.sumV)) +
geom_point(aes(color=Fraction), size=2, position=pd) +
geom_errorbar(aes(ymin=mean.sumV-SD, ymax=mean.sumV+SD, color=Fraction),
size=.5, width=0, position=pd) +
coord_cartesian(ylim=c(0, 3))+
ggtitle(expression(paste("Sum-V ", delta^{15}, N[AA]))) +
scale_color_manual(values=frac.color) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed", "Plankton")) +
ylab(expression(paste(delta^{15}, N[Sum-V], " (\u2030, Air)"))) +
Fig.formatting
#print(sumV)
#ggsave("figures/nitrogen/d15N.sumV.CSIA.pdf", height=5, width=8, encod="MacRoman")
These are the weighted means for trophic and source AA following Bradley et al. 2015. Weighted mean AA δ15N values.
###########################
##################
#########
df.mean<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, mean, na.rm=TRUE)
df.n<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, length)
df.SD<-aggregate(d15N.value~AA.short+AA.cat+Fraction+Treat.Int, data=dfN, sd, na.rm=TRUE)
colnames(df.SD)[5]="SD"
df.mean<-cbind(df.mean, df.SD[5])
df.mean$mean.sd<-(df.mean$d15N.value/df.mean$SD)
df.mean$inv.sd<-(1/df.mean$SD)
# write.csv(df.mean, "wtmeans.csv") # use this to calculate weighted mean
# weighted mean is sum(mean.sd/inv.sd) for trophic AA, same for source AA
# delta.Tr.So (below) is difference in (weighted mean) Trophic AA - Source AA for host or symb, per treatment
#########
wt.mean<-read.csv("data/wt.means.d15N.csv")
wt.mean.df<-aggregate(wt.mean~AA.cat+Fraction+Treat.Int, data=wt.mean, mean, na.rm=TRUE)
SD<-aggregate(wt.SD~AA.cat+Fraction+Treat.Int, data=wt.mean, mean, na.rm=TRUE); colnames(SD)[4]<-"wt.SD"
wt.mean.df<-cbind(wt.mean.df, SD[4])
######## model
wt.mean.mod<-wt.mean[!(wt.mean$Fraction=="plank"),]
anova(lm(wt.mean~Fraction + Treat.Int, data=wt.mean.mod)) # no difference
########
pd <- position_dodge(0.5) #offset for error bars
ggplot(wt.mean.df, aes(x=Treat.Int, y=wt.mean)) +
geom_point(aes(color=Fraction, shape=AA.cat), size=3, position=pd) +
geom_errorbar(aes(ymin=wt.mean-wt.SD, ymax=wt.mean+wt.SD, color=Fraction, shape=AA.cat),
size=.5, width=0, position=pd) +
coord_cartesian(ylim=c(0, 12))+
ggtitle(expression(paste("Weighted Mean ", delta^{15}, N[AA]))) +
scale_color_manual(values=c("coral", "springgreen3")) +
scale_x_discrete(name ="Treatments",
labels=c("Dark\nFed", "Light\nFed", "Light\nNot Fed")) +
ylab(expression(paste(delta^{15}, N[AA], " (\u2030, Air)"))) +
Fig.formatting
Figure S4. δ15NAA weighted means for trophic and source amino acids according to coral tissue fractions (host, symbiont) and experimental nutrition-treatments. Values are mean ± SD (n=2).
ggsave("figures/Fig S4.wt.mean.d15N.CSIA.pdf", height=5, width=8, encod="MacRoman")
Figure 2– PCA.combined
#### compile the 4 PCA ###
library("cowplot")
plot_grid(PC.fig3.C.frac, PC.fig4.N.frac, PC.fig1.C.trt, PC.fig2.N.trt, ncol = 2)
Figure 2. Principal component analysis of carbon (left) and nitrogen (right) isotope values of individual amino acids in corals, Symbiodiniaceae, and a pooled plankton sample in relation to tissue fraction (top) and treatments (bottom). Ellipses represent 90% standard deviation with arrows for individual amino acids being significant (p<0.05) correlation vectors.
ggsave("figures/Fig 2.PCAs.pdf", height=8, width=11, encod="MacRoman")
######
Figure xx– LDA.combined
#### compile the 4 LDA ###
plot_grid(LDA.C.NEAA, LDA.N.Tr, LDA.C.EAA, LDA.N.Sr, ncol = 2)
ggsave("figures/Fig x.LDAs.pdf", height=6, width=7, encod="MacRoman")
######
Figure 3– AA.frac-by-treatment combined
leg1 <- get_legend(
# create some space to the left of the legend
d13C.CSIA_frac.trt + theme(legend.box.margin = margin(0, 0, 0, 12)))
AA.frac.trt<- plot_grid(d13C.CSIA_frac.trt + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(a)", x=1, y=0, color="black"),
d15N.CSIA_frac.trt + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(b)", x=1, y=15, color="black"),
ncol=2,nrow=1)
plot_grid(AA.frac.trt, leg1, rel_widths = c(8, 1)) # legend 1/8 size as first obj.
Figure 3. (a) Carbon and (b) nitrogen isotope analysis of individual amino acids in coral (host), symbiont algae (symb), and a pooled plankton sample (plank) and in host and symbiont tissues in response to nutrition treatment: Dark-Fed (D-F), Light-Fed (L-F), and Light-Not Fed (L-NF). Values are mean ± SD (n=2), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig 3.AA.frac.trt.pdf", width = 10, height = 5, encod="MacRoman")
dev.off()
Supplemental Figure S1– AA.trt plot
leg2 <- get_legend(
# create some space to the left of the legend
d13C.CSIA_Trt.alone + theme(legend.box.margin = margin(0, 0, 0, 12)))
AA.trt<- plot_grid(d13C.CSIA_Trt.alone + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(a)", x=1, y=0, color="black"),
d15N.CSIA_Trt.alone + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(b)", x=1, y=15, color="black"),
ncol=2,nrow=1)
plot_grid(AA.trt, leg2, rel_widths = c(8, 1)) # legend 1/8 size as first obj.
Figure S1. (a) Carbon and (b) nitrogen isotope analysis of individual amino acids in coral (host), symbiont algae (symb), and a pooled plankton sample (plank). Values are mean ± SD (n=2), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig S1.AA.trt.pdf", width = 10, height = 5, encod="MacRoman")
dev.off()
Supplemental Figure S2– AA.frac combined
leg3 <- get_legend(
# create some space to the left of the legend
d13C.CSIA_Fraction + theme(legend.box.margin = margin(0, 0, 0, 12)))
AA.trt<- plot_grid(d13C.CSIA_Fraction + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(a)", x=1, y=0, color="black"),
d15N.CSIA_Fraction + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=3, label="(b)", x=1, y=15, color="black"),
ncol=2,nrow=1)
plot_grid(AA.trt, leg3, rel_widths = c(8, 1)) # legend 1/8 size as first obj.
Figure S2. (a) Carbon and (b) nitrogen isotope analysis of individual amino acids in coral (host), symbiont algae (symb), and a pooled plankton sample (plank). Values are mean ± SE (n=6), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig S2.AA.frac.pdf", width = 10, height = 5, encod="MacRoman")
dev.off()
Supplemental figure S3– TP and Sum V combined
leg4 <- get_legend(
# create some space to the left of the legend
TP + theme(legend.box.margin = margin(0, 0, 0, 12)))
TP.sumV.plots<- plot_grid(TP + ggtitle("")+
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=2.2, label="(a)", x=0.7, y=3, color="black"),
sumV + ggtitle("") +
theme(legend.position = "none",
axis.text.x=element_text(size=7),
axis.text.y=element_text(size=7),
axis.title.x=element_text(size=7),
axis.title.y=element_text(size=7)) +
annotate(geom="text", size=2.2, label="(b)", x=0.7, y=3, color="black"),
ncol=2,nrow=1)
plot_grid(TP.sumV.plots, leg4, rel_widths = c(6, 1)) # legend 1/3 size as first obj.
Figure S3. (a) Trophic position and (b) sum-V calculations for nitrogen isotope analysis of individual amino acids in coral (host) and symbiont algae (symb) in response to nutrition treatment and in relation to a pooled plankton sample (plank). Values are mean ± SD (n=2), except for the plankton sample (n=1).
dev.copy(pdf, "figures/Fig S3.TP.sumV.pdf", width = 7, height = 5, encod="MacRoman")
dev.off()
Mean-normalized data is important for comparing across species, times, or locations. This is accomplished by taking the across sample mean for the amino acid (AA) of choice, then subtracting the raw AA value from the mean. We did this for the essential AA (EAA) of carbon: Leucine, Isoleucine, Phenylalanine, Threonine, Lysine, Valine.
Uses linear discrimination analyses (LDA) of the mean-normalized EAA in symbiont and plankton+POM from Oahu (this study) and those reported in Fox et al (2019, FuncEcol) to act as training data for sources of autotrophic vs. heterotrophic nutrition. The training data is then applied to the coral species in the two studies (M. capitata and Pocillopora meandrina) to assign coral IDs, asking if the corals align with either autotrophic or heterotrophic groups.
Combine the data from Oahu and Palmyra ### d13C-AA data from Fox et al
##### Palmyra data
Fox.data<-read.csv("data/Palmyra_AAd13C.csv")
# rename a column
names(Fox.data)[names(Fox.data)=="group"] <- "Fraction"
# rename a level
Fox.data$Fraction<- revalue(Fox.data$Fraction, c("Animal"="host.Pal", "Zoox"="symb.Pal",
"Plankton"="plank.Pal", "POM"="POM.Pal"))
# select columns
Fox.data<- Fox.data %>%
select(Location, Species, Fraction, Ala13C, Gly13C, Thr13C, Ser13C, Val13C, Leu13C, Ile13C, Pro13C, Asp13C, Glu13C, Phe13C, Tyr13C, Lys13C)
# rename AA
Fox.data<- plyr::rename(Fox.data, c("Ala13C"="Ala", "Gly13C"="Gly", "Thr13C" = "Thr",
"Ser13C" = "Ser", "Val13C" = "Val", "Leu13C" = "Leu",
"Ile13C" = "Ile", "Pro13C"= "Pro", "Asp13C" = "Asp",
"Glu13C" = "Glu", "Phe13C" = "Phe", "Tyr13C" = "Tyr", "Lys13C" = "Lys"))
#### Kaneohe, Oahu data
Wall.data<-d13C.dat
# add a Location column
Wall.data$Location<- "Oahu"
# rename a level
Wall.data$Fraction<- revalue(Wall.data$Fraction, c("host"="host.HI", "symb"="symb.HI",
"plank"="plank.HI"))
# select columns
Wall.data<-Wall.data %>%
select(Location, Species, Fraction, Alanine, Glycine, Threonine, Serine, Valine, Leucine, Isoleucine, Proline, Aspartic.acid, Glutamic.acid, Phenylalanine, Tyrosine, Lysine)
# make names the same for both df
Wall.data<- plyr::rename(Wall.data, c("Alanine"="Ala", "Glycine"="Gly", "Threonine" = "Thr",
"Serine" = "Ser", "Valine" = "Val", "Leucine" = "Leu",
"Isoleucine" = "Ile", "Proline"= "Pro", "Aspartic.acid" = "Asp",
"Glutamic.acid" = "Glu", "Phenylalanine" = "Phe",
"Tyrosine" = "Tyr", "Lysine" = "Lys"))
# combine the dataframes
Pacific.AA<-rbind(Fox.data, Wall.data)
Pacific.AA$Fraction<-factor(Pacific.AA$Fraction, levels=c("host.Pal", "host.HI", "symb.Pal", "symb.HI",
"plank.Pal", "plank.HI", "POM.Pal"))
Run LDA analysis as performed in Fox et al.
######## For manuscript
####### using normalized data
####### run LDA with 2 sources: (1) plankton+POM, (2) symbiont Palmyra
####### use the 2 sources as training data for Hawaii
####### display points according to location (Palmyra or Hawaii)
##########
###########
### Raw data = 'Pacific.AA'
# LDA palmyra POM + plank, with Hawaii dataset
# 6 EAA
Pacific.EAA.df<- Pacific.AA %>%
select(Location, Species, Fraction, Leu, Ile, Phe, Thr, Lys, Val)
###### ###### ###### ###### ######
###### NORMALIZING AAESS DATA BY Sample MEAN (mean of all AA d13C) - raw individual AAd13C value
# raw compiled data is 'Pacific.EAA.df'
Pac.ess.norm<-Pacific.EAA.df
#make ID column to run the normalization
Pac.ess.norm$ID<-1:nrow(Pac.ess.norm)
for(i in 1:length(Pac.ess.norm$ID)){
Pac.ess.norm$Ile.n[i] <- (Pac.ess.norm$Ile[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Leu.n[i] <- (Pac.ess.norm$Leu[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Lys.n[i] <- (Pac.ess.norm$Lys[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Phe.n[i] <- (Pac.ess.norm$Phe[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Thr.n[i] <- (Pac.ess.norm$Thr[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
Pac.ess.norm$Val.n[i] <- (Pac.ess.norm$Val[i]-mean(as.numeric(Pac.ess.norm[i,4:9])))
}
Pac.ess.norm<-Pac.ess.norm[,c(1:3,10:16)] # drop raw columns and reorder
###############
############### Normalized LDA data is 'Pac.ess.norm.df'
# add new column of factors
Pac.ess.norm$Fraction.grouped<-Pac.ess.norm$Fraction
#rename these factors to animal, symbiont, plankton, POM
Pac.ess.norm$Fraction.grouped<-revalue(Pac.ess.norm$Fraction.grouped,
c("host.Pal"="animal", "host.HI"="animal",
"symb.Pal"="symbiont", "symb.HI"="symbiont",
"plank.Pal"="plankton", "plank.HI"="plankton",
"POM.Pal"="POM"))
#reorder
Pac.ess.norm.df<-Pac.ess.norm[,c(1:3,11,4:10)]
# separate out animal
# plankton, POM, symb = "zp"
Pac.EAA.norm.zp<-
Pac.ess.norm.df[!(Pac.ess.norm.df$Fraction.grouped=="animal"),]
# host animal = "ho"
Pac.EAA.norm.ho<-
Pac.ess.norm.df[(Pac.ess.norm.df$Fraction.grouped=="animal"),]
# drop factor levels
Pac.EAA.norm.zp<-droplevels(Pac.EAA.norm.zp)
Pac.EAA.norm.ho<-droplevels(Pac.EAA.norm.ho)
# remove ID columns for now
Pac.EAA.frac.norm.zp<-Pac.EAA.norm.zp[,c(-1:-3,-5)]
Pac.EAA.frac.norm.ho<-Pac.EAA.norm.ho[,c(-1:-3,-5)]
############
############ training for leave one out: how well can we estimate the sources?
# with CV = true, results for classes and posteriors are for cross validation (CV, leave one out)
LDA.Pac.EAA.frac.norm.zp <- lda(Fraction.grouped ~ Ile.n + Leu.n + Lys.n + Phe.n + Thr.n + Val.n, data = Pac.EAA.frac.norm.zp, CV = TRUE)
# create a table which compares the classifcation of the LDA model to the actual spp
ct.prod.norm <- table(Pac.EAA.frac.norm.zp$Fraction.grouped, LDA.Pac.EAA.frac.norm.zp$class)
# total percent of samples correctly classified is the sum of the diagonal of this table
sum(diag(prop.table(ct.prod.norm))) #71% effective
## [1] 0.7142857
# what % of each species is being correctly classified
diag(prop.table(ct.prod.norm, 1)) # symbionts 100% classified, plankton 89%
## symbiont plankton POM
## 1.00 0.60 0.25
# create a training lda function from the source data - use this to classify the coral hosts
training.EAA.norm.samples <- lda(Fraction.grouped ~ Ile.n + Leu.n + Lys.n + Phe.n + Thr.n + Val.n, data = Pac.EAA.frac.norm.zp)
#examine coefficents of the discriminants to determine AAs contributing to groups seperation
training.EAA.norm.samples$scaling # most for Valine > Leucine > Isoleucine > Phenylalanine
## LD1 LD2
## Ile.n -0.505236200 0.73939336
## Leu.n 0.547650386 0.33476977
## Lys.n 0.001763107 0.32016526
## Phe.n -0.348097550 -0.41508277
## Thr.n -0.122020249 -0.22064176
## Val.n 0.641733096 0.06041539
# create a dataframe with these LDA coordinates
datPred.norm.zp <- data.frame(Fraction.grouped=Pac.EAA.frac.norm.zp$Fraction.grouped,
predict(training.EAA.norm.samples)$x) #create data.frame
datPred.norm.zp$ID<-Pac.EAA.norm.zp$ID
# predict the coral animal fractions based on ess
host.norm.res <- predict(training.EAA.norm.samples, Pac.EAA.frac.norm.ho) # 6 classified as plankton-POM
#save the predicted coordinates
datPred2.norm.ho <- data.frame(Fraction.grouped='animal', host.norm.res$x)
datPred2.norm.ho$ID<-Pac.EAA.norm.ho$ID
# add to the original animal dataframe which individual got classified as what:
Pac.EAA.norm.ho$class <- host.norm.res$class #--- only 2 classified as non-symb
# merge the source and coral animal dataframes for plotting
datPred3.norm.Pac <- rbind(datPred.norm.zp, datPred2.norm.ho)
#re-attach medata by merging with ID
# original df with palmyra corals and zoox dropped = "Pac.EAA.frac.df"
EAA.frac.LD.norm.meta<-merge(datPred3.norm.Pac, Pac.ess.norm.df, by="ID")
names(EAA.frac.LD.norm.meta)[names(EAA.frac.LD.norm.meta)=="Fraction.grouped.x"] <- "Fraction.grouped"
#remove redundant group
EAA.frac.LD.norm.meta<-EAA.frac.LD.norm.meta[,-8]
EAA.frac.LD.norm.meta$Fraction.source2<-EAA.frac.LD.norm.meta$Fraction.grouped
EAA.frac.LD.norm.meta$Fraction.source2<-revalue(EAA.frac.LD.norm.meta$Fraction.source2,
c("POM"="plankton"))
EAA.frac.LD.norm.meta<-droplevels(EAA.frac.LD.norm.meta)
# for ellipses
dat_ell.norm.EAA <- data.frame()
for(g in levels(EAA.frac.LD.norm.meta$Fraction.source2)){
dat_ell.norm.EAA <- rbind(dat_ell.norm.EAA,
cbind(as.data.frame(with
(EAA.frac.LD.norm.meta[EAA.frac.LD.norm.meta$Fraction.source2==g,],
ellipse(cor(LD1, LD2),
scale=c(sd(LD1),sd(LD2)),
centre=c(mean(LD1),mean(LD2))))), Fraction.source2=g))
}
# no ellipse drawn for animal
dat_ell.norm.EAA.source<-dat_ell.norm.EAA[!(dat_ell.norm.EAA$Fraction.source2=="animal"),]
# add back Location column
EAA.frac.LD.norm.meta$Location<-Pac.ess.norm.df$Location
# order factor
EAA.frac.LD.norm.meta$Fraction.grouped<-factor(EAA.frac.LD.norm.meta$Fraction.grouped,
levels=c("animal", "symbiont", "plankton", "POM"))
# Fraction colors
frac.color<-c("coral", "springgreen3", "steelblue1", "skyblue4")
LDA.EAA.frac.norm<-ggplot(EAA.frac.LD.norm.meta, aes(LD1, LD2)) +
geom_point(aes(color = Fraction.grouped, shape=Location), size=2)+
scale_color_manual(values=frac.color) +
scale_shape_manual(values=c(1,16))+
geom_path(data=dat_ell.norm.EAA.source, aes(x=x,y=y,color=Fraction.source2),
size=0.5,linetype=2, show.legend=FALSE) +
theme(axis.ticks.length=unit(0.25, "cm"),
axis.text.y=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm")),
axis.text.x=element_text(margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm"))) +
theme(text = element_text(size=8)) +
theme(legend.text=element_text(size=10), legend.key = element_blank()) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black", size=0.5))
print(LDA.EAA.frac.norm)
Figure 4. Linear discriminant analysis of carbon isotope fingerprints using mean-normalized values of six essential amino acids (isoleucine, leucine, lysine, phenylalanine, threonine, tyrosine, valine). Ellipses represent 95% confidence ellipses for each nutrition group (autotrophy [symbiont] or heterotrophy [plankton-POM). Hawaii data are host (n=6), symbiont (n=6) and plankton (n=1); Palmyra data are host (n = 19), symbionts (n=11), plankton (n=9) and POM (n=8) (Fox et al. 2019).
ggsave("figures/LDA.EAA.norm.2source.pdf", height=5, width=5)
Compare the mean values of d13C-AA for samples of plankton and POM in Palmyra (Fox et al, 2019) with those of our study. This is Figure S5
# summary of means, pretty close
library(dplyr)
plank.meta<-Pacific.AA[(Pacific.AA$Fraction=="plank.HI" |
Pacific.AA$Fraction=="plank.Pal" |
Pacific.AA$Fraction=="POM.Pal"),]
plank.meta<-plank.meta[,c(-1:-2)] # remove extra columns
AA.means.mean<-plank.meta %>%
na.omit() %>%
group_by(Fraction) %>%
summarise_all("mean")
# melt column data to rows
md <- melt(plank.meta, id=("Fraction"))
colnames(md)<-c("Fraction", "AA", "d13C.AA")
md$Fraction<-droplevels(md$Fraction)
plank.metadf<-md
# drop levels to just Palmyra and Hawaii
plank.metadf$Fraction<-revalue(plank.metadf$Fraction, c("plank.Pal"="Plankton Palmyra", "POM.Pal"="POM Palmyra", "plank.HI" = "Plankton Hawaii"))
df.mean.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, mean, na.rm=TRUE)
df.SE.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, na.rm=TRUE, std.error)
df.n.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, length)
df.SE.pl[is.na(df.SE.pl)] <- 0
colnames(df.SE.pl)[3]="SE"
df.mean.pl<-cbind(df.mean.pl, df.SE.pl[3])
pd <- position_dodge(0.5) #offset for error bars
d13C.plank.POM.meta<-ggplot(df.mean.pl, aes(x=AA, y=d13C.AA, group=Fraction, shape=Fraction)) +
geom_errorbar(aes(ymin=d13C.AA-SE, ymax=d13C.AA+SE, color=Fraction), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction), size=2, position=pd) +
ggtitle(expression(paste(delta^{13}, C[AA], " plankton"))) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=2.5, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=2.5, color="gray40") +
coord_cartesian(ylim=c(-35, 3)) +
xlab("Amino Acids") +
scale_color_manual(values=c("lightgoldenrod4", "mediumorchid", "darkolivegreen3")) +
scale_shape_manual(values=c(19,3,19))+
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
Fig.formatting
print(d13C.plank.POM.meta)
Figure S5. Comparison of δ13C values for non-essential and essential amino acids in a pooled plankton sample from Kāne‘ohe Bay, O‘ahu, Hawai‘i and plankton and POM from Palmyra (reported in (Fox et al. 2019)). Values are mean ± SE (n=6 - 9), except for the Hawai‘i plankton (n=1).
plot_grid(d13C.plank.POM.meta)
Figure S5. Comparison of δ13C values for non-essential and essential amino acids in a pooled plankton sample from Kāne‘ohe Bay, O‘ahu, Hawai‘i and plankton and POM from Palmyra (reported in (Fox et al. 2019)). Values are mean ± SE (n=6 - 9), except for the Hawai‘i plankton (n=1).
ggsave("figures/Fig S5. Pacific.plank.means.pdf", height=5, width=6, encod="MacRoman")
######
# drop levels to just Palmyra and Hawaii
plank.metadf$Fraction<-revalue(plank.metadf$Fraction,
c("Plankton Palmyra"="Palmyra", "POM Palmyra"="Palmyra", "Plankton Hawaii"="Hawaii"))
df.mean.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, mean, na.rm=TRUE)
df.SE.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, na.rm=TRUE, std.error)
df.n.pl<-aggregate(d13C.AA~Fraction+AA, data=plank.metadf, length)
df.SE.pl[is.na(df.SE.pl)] <- 0
colnames(df.SE.pl)[3]="SE"
df.mean.pl<-cbind(df.mean.pl, df.SE.pl[3])
pd <- position_dodge(0.5) #offset for error bars
d13C.plank.meta<-ggplot(df.mean.pl, aes(x=AA, y=d13C.AA, group=Fraction)) +
geom_errorbar(aes(ymin=d13C.AA-SE, ymax=d13C.AA+SE, color=Fraction), size=.5, width=0, position=pd) +
geom_point(aes(color=Fraction), size=2, position=pd) +
ggtitle(expression(paste(delta^{13}, C[AA], " plankton"))) +
geom_vline(xintercept=7.5, linetype="solid", color = "gray") +
annotate(geom="text", label="Nonessential-AA", x=4, y=2.5, color="gray40") +
annotate(geom="text", label="Essential-AA", x=10, y=2.5, color="gray40") +
coord_cartesian(ylim=c(-35, 3)) +
xlab("Amino Acids") +
scale_color_manual(values=c("darkolivegreen4", "mediumorchid")) +
ylab(expression(paste(delta^{13}, C[AA], " (\u2030, V-PDB)"))) +
Fig.formatting
#print(d13C.plank.meta)